I will do computer vision, object detection, yolo, opencv projects

I conduct a feasibility assessment using sample data to establish mAP, accuracy, and FPS benchmarks. Every computer vision model is rigorously tested and optimized for low latency before delivery, ensuring your vision system meets real-world production performance requirements.

I develop computer vision applications using Python, PyTorch, TensorFlow, and OpenCV. For optimization and deployment, I utilize ONNX, TensorRT, and Docker. This ensures your computer vision pipeline runs with high accuracy and low latency across both cloud and edge setups.

You can supply your own images or videos, or I can assist with dataset collection and annotation using tools like Roboflow. If you lack data, we can also leverage relevant public benchmark datasets or generate synthetic data tailored to your project.

Yes. You will receive 100% ownership of the clean, documented Python source code, setup instructions, and all trained model weights (.pt, .onnx, or .engine files) ready for immediate deployment.

Absolutely. I wrap models in FastAPI microservices, containerize them using Docker, and build web interfaces with Streamlit. I also optimize model inference using TensorRT and ONNX Runtime for edge devices like NVIDIA Jetson or Raspberry Pi.

Yes. I build multi-threaded pipelines optimized specifically for real-time RTSP, webcam, and IP camera feeds, ensuring high frame rates (FPS) and low latency using GPU hardware acceleration.

Yes, please message me first! Computer vision projects vary in dataset quality, model complexity, and hardware targets. A brief conversation helps us establish technical feasibility and select the optimal package or custom quote for your needs.